4 papers
Optimal Experimental Design of a Moving Sensor for Linear Bayesian Inverse Problems
Nicole Aretz, Thomas Lynn, Karen Willcox +1
We optimize the path of a mobile sensor to minimize the posterior uncertainty of a Bayesian inverse problem. Along its path, the sensor continuously takes measurements of the state…
Nested Operator Inference for Adaptive Data-Driven Learning of Reduced-order Models
Nicole Aretz, Karen Willcox
This paper presents a data-driven, nested Operator Inference (OpInf) approach for learning physics-informed reduced-order models (ROMs) from snapshot data of high-dimensional dynam…
Non-intrusive reduced-order modeling for dynamical systems with spatially localized features
Leonidas Gkimisis, Nicole Aretz, Marco Tezzele +3
This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed…
Multifidelity Uncertainty Quantification for Ice Sheet Simulations
Nicole Aretz, Max Gunzburger, Mathieu Morlighem +1
Ice sheet simulations suffer from vast parametric uncertainties, such as the basal sliding boundary condition or geothermal heat flux. Quantifying the resulting uncertainties in pr…